Interpretive Summary: Global change field experiments have now been conducted across the globe. One of the key parameters often measured in these experiments is the loss of CO2 from the soil surface to the atmosphere, commonly referred to as ‘soil respiration’. Global change experiments often produce quantitative relationships between soil respiration and environment, such as soil moisture or soil temperature, but it is unclear whether these relationships adequately predict changes in soil respiration in response to actual temporal variability in weather and rainfall. This paper attempts to fill this gap by analyzing soil temperature, soil moisture, and soil respiration data from 52 precipitation manipulation experiments. The study found that relationships developed from experimental treatments often inadequately predicted soil respiration responses to actual variability over time, in part because of inadequate soil respiration measurement frequency. This result provides a basis for improved experimental design that will support improved prediction of ecosystem responses to climatic variability expected with future climate change. These predictions, in turn, will enhance our ability to adapt to and mitigate future climate change impacts.

Technical Abstract:
As a key component of the carbon cycle, soil respiration (Rsoil) is being excessively studied with the aim of improving our understanding as well as our ability to predict Rsoil when climate changes. Many manipulation experiments have been performed to test how Rsoil and other carbon fluxes and ecosystem processes may respond to climatic changes such as CO2 enrichment, warming and changes in precipitation patterns. Nonetheless, it remains largely unknown to what extent current relations between abiotic factors and ecosystem processes can be used for extrapolation over time. In the current study, we tested if current responses of Rsoil to fluctuations in soil temperature (Tsoil) and volumetric soil water content (SWC) can be used to predict Rsoil when rainfall patterns alter. To this end, we used data from 52 precipitation manipulation experiments and tested whether a model parameterized for the control plots (using soil Tsoil and SWC as predictor variables) could predict Rsoil measured in the treatment plots. This analysis revealed that such extrapolation was not acceptable for 27 experiments, and particularly not for experiments in which Rsoil was measured more frequently. Based on analysis of the high frequency (daily) data, we further demonstrated that robustness of the results decreased with decreasing measurement frequency and measurement intervals of less one week are desirable. Across our dataset, under- and overestimations of Rsoil in the treatment ranged between -50 and +270%. In this review, we list a number of mechanisms that can alter Rsoil and its relation to Tsoil and SWC when rainfall patterns alter.